OpenRaiser/Intern-Atlas huggingface dataset:
From Biological Synapses to Intelligent Robots
Venue: arXiv cs.RO 2022
Year: 2022

This review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the highly plastic biological neural networks of invertebrates and vertebrates. Its potential for adaptive learning and control without supervision, the generation of functional complexity, and control architectures based on self organization is brought forward. Learning without prior knowledge based on excitatory and inhibitory neural mechanisms accounts for the process through which survival or task relevant representations are either reinforced or suppressed. The basic mechanisms of unsupervised biological learning drive synaptic plasticity and adaptation for behavioral success in living brains with different levels of complexity. The insights collected here point toward the Hebbian model as a choice solution for intelligent robotics and sensor systems. Keywords: Hebbian learning, synaptic plasticity, neural networks, self organization, brain, reinforcement, sensory processing, robot control
Online handwriting, signature and touch dynamics: tasks and potential applications in the field of security and health
Venue: arXiv cs.CR 2022
Year: 2022

Background: An advantageous property of behavioural signals ,e.g. handwriting, in contrast to morphological ones, such as iris, fingerprint, hand geometry, etc., is the possibility to ask a user for a very rich amount of different tasks. Methods: This article summarises recent findings and applications of different handwriting and drawing tasks in the field of security and health. More specifically, it is focused on on-line handwriting and hand-based interaction, i.e. signals that utilise a digitizing device (specific devoted or general-purpose tablet/smartphone) during the realization of the tasks. Such devices permit the acquisition of on-surface dynamics as well as in-air movements in time, thus providing complex and richer information when compared to the conventional pen and paper method. Conclusions: Although the scientific literature reports a wide range of tasks and applications, in this paper, we summarize only those providing competitive results (e.g. in terms of discrimination power) and having a significant impact in the field.
Integrating Immersive Technologies for Algorithmic Design in Architecture
Venue: arXiv cs.HC 2022
Year: 2022

Architectural design practice has radically evolved over the course of its history, due to technological improvements that gave rise to advanced automated tools for many design tasks. Traditional paper drawings and scale models are now accompanied by 2D and 3D Computer-Aided Architectural Design (CAAD) software. While such tools improved in many ways, including performance and accuracy improvements, the modalities of user interaction have mostly remained the same, with 2D interfaces displayed on 2D screens. The maturation of Augmented Reality (AR) and Virtual Reality (VR) technology has led to some level of integration of these immersive technologies into architectural practice, but mostly limited to visualisation purposes, e.g. to show a finished project to a potential client. We posit that there is potential to employ such technologies earlier in the architectural design process and therefore explore that possibility with a focus on Algorithmic Design (AD), a CAAD paradigm that relies on (often visual) algorithms to generate geometries. The main goal of this dissertation is to demonstrate that AR and VR can be adopted for AD activities. To verify that claim, we follow an iterative prototype-based methodology to develop research prototype software tools and evaluate them. The three developed prototypes provide evidence that integrating immersive technologies into the AD toolset provides opportunities for architects to improve their workflow and to better present their creations to clients. Based on our contributions and the feedback we gathered from architectural students and other researchers that evaluated the developed prototypes, we additionally provide insights as to future perspectives in the field.
Short Paper: Device- and Locality-Specific Fingerprinting of Shared NISQ Quantum Computers
Venue: arXiv cs.CR 2022
Year: 2022

Fingerprinting of quantum computer devices is a new threat that poses a challenge to shared, cloud-based quantum computers. Fingerprinting can allow adversaries to map quantum computer infrastructures, uniquely identify cloud-based devices which otherwise have no public identifiers, and it can assist other adversarial attacks. This work shows idle tomography-based fingerprinting method based on crosstalk-induced errors in NISQ quantum computers. The device- and locality-specific fingerprinting results show prediction accuracy values of $99.1\%$ and $95.3\%$, respectively.
'Cyber security is a dark art': The CISO as soothsayer
Venue: arXiv cs.CY 2022
Year: 2022

Commercial organisations continue to face a growing and evolving threat of data breaches and system compromises, making their cyber-security function critically important. Many organisations employ a Chief Information Security Officer (CISO) to lead such a function. We conducted in-depth, semi-structured interviews with 15 CISOs and six senior organisational leaders, between October 2019 and July 2020, as part of a wider exploration into the purpose of CISOs and cyber-security functions. In this paper, we employ broader security scholarship related to ontological security and sociological notions of identity work to provide an interpretative analysis of the CISO role in organisations. Research findings reveal that cyber security is an expert system that positions the CISO as an interpreter of something that is mystical, unknown and fearful to the uninitiated. They show how the fearful nature of cyber security contributes to it being considered an ontological threat by the organisation, while responding to that threat contributes to the organisation's overall identity. We further show how cyber security is analogous to a belief system and how one of the roles of the CISO is akin to that of a modern-day soothsayer for senior management; that this role is precarious and, at the same time, superior, leading to alienation within the organisation. Our study also highlights that the CISO identity of protector-from-threat, linked to the precarious position, motivates self-serving actions that we term `cyber sophistry'. We conclude by outlining a series of implications for both organisations and CISOs.
Equilibrium Aggregation: Encoding Sets via Optimization
Venue: arXiv cs.LG 2022
Year: 2022

Processing sets or other unordered, potentially variable-sized inputs in neural networks is usually handled by aggregating a number of input tensors into a single representation. While a number of aggregation methods already exist from simple sum pooling to multi-head attention, they are limited in their representational power both from theoretical and empirical perspectives. On the search of a principally more powerful aggregation strategy, we propose an optimization-based method called Equilibrium Aggregation. We show that many existing aggregation methods can be recovered as special cases of Equilibrium Aggregation and that it is provably more efficient in some important cases. Equilibrium Aggregation can be used as a drop-in replacement in many existing architectures and applications. We validate its efficiency on three different tasks: median estimation, class counting, and molecular property prediction. In all experiments, Equilibrium Aggregation achieves higher performance than the other aggregation techniques we test.
Benchmark Assessment for DeepSpeed Optimization Library
Venue: arXiv cs.LG 2022
Year: 2022

Deep Learning (DL) models are widely used in machine learning due to their performance and ability to deal with large datasets while producing high accuracy and performance metrics. The size of such datasets and the complexity of DL models cause such models to be complex, consuming large amount of resources and time to train. Many recent libraries and applications are introduced to deal with DL complexity and efficiency issues. In this paper, we evaluated one example, Microsoft DeepSpeed library through classification tasks. DeepSpeed public sources reported classification performance metrics on the LeNet architecture. We extended this through evaluating the library on several modern neural network architectures, including convolutional neural networks (CNNs) and Vision Transformer (ViT). Results indicated that DeepSpeed, while can make improvements in some of those cases, it has no or negative impact on others.
DataLab: A Platform for Data Analysis and Intervention
Venue: arXiv cs.LG 2022
Year: 2022

Despite data's crucial role in machine learning, most existing tools and research tend to focus on systems on top of existing data rather than how to interpret and manipulate data. In this paper, we propose DataLab, a unified data-oriented platform that not only allows users to interactively analyze the characteristics of data, but also provides a standardized interface for different data processing operations. Additionally, in view of the ongoing proliferation of datasets, \toolname has features for dataset recommendation and global vision analysis that help researchers form a better view of the data ecosystem. So far, DataLab covers 1,715 datasets and 3,583 of its transformed version (e.g., hyponyms replacement), where 728 datasets support various analyses (e.g., with respect to gender bias) with the help of 140M samples annotated by 318 feature functions. DataLab is under active development and will be supported going forward. We have released a web platform, web API, Python SDK, PyPI published package and online documentation, which hopefully, can meet the diverse needs of researchers.
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference
Venue: arXiv cs.LG 2022
Year: 2022

Gaussian process latent variable models (GPLVM) are a flexible and non-linear approach to dimensionality reduction, extending classical Gaussian processes to an unsupervised learning context. The Bayesian incarnation of the GPLVM Titsias and Lawrence, 2010] uses a variational framework, where the posterior over latent variables is approximated by a well-behaved variational family, a factorized Gaussian yielding a tractable lower bound. However, the non-factories ability of the lower bound prevents truly scalable inference. In this work, we study the doubly stochastic formulation of the Bayesian GPLVM model amenable with minibatch training. We show how this framework is compatible with different latent variable formulations and perform experiments to compare a suite of models. Further, we demonstrate how we can train in the presence of massively missing data and obtain high-fidelity reconstructions. We demonstrate the model's performance by benchmarking against the canonical sparse GPLVM for high-dimensional data examples.
Wearable uBrain: Fabric Based-Spiking Neural Network
Venue: arXiv cs.HC 2022
Year: 2022

On garment intelligence influenced by artificial neural networks and neuromorphic computing is emerging as a research direction in the e-textile sector. In particular, bio inspired Spiking Neural Networks mimicking the workings of the brain show promise in recent ICT research applications. Taking such technological advancements and new research directions driving forward the next generation of e-textiles and smart materials, we present a wearable micro Brain capable of event driven artificial spiking neural network computation in a fabric based environment. We demonstrate a wearable Brain SNN prototype with multi-layer computation, enabling scalability and flexibility in terms of modifications for hidden layers to be augmented to the network. The wearable micro Brain provides a low size, weight and power artificial on-garment intelligent wearable solution with embedded functionality enabling offline adaptive learning through the provision of interchangeable resistor synaptic weightings. The prototype has been evaluated for fault tolerance, where we have determine the robustness of the circuit when certain parts are damaged. Validations were also conducted for movements to determine if the circuit can still perform accurate computation.
Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms
Venue: arXiv cs.LG 2022
Year: 2022

We study a sequential decision problem where the learner faces a sequence of $K$-armed bandit tasks. The task boundaries might be known (the bandit meta-learning setting), or unknown (the non-stationary bandit setting). For a given integer $M\le K$, the learner aims to compete with the best subset of arms of size $M$. We design an algorithm based on a reduction to bandit submodular maximization, and show that, for $T$ rounds comprised of $N$ tasks, in the regime of large number of tasks and small number of optimal arms $M$, its regret in both settings is smaller than the simple baseline of $\tilde{O}(\sqrt{KNT})$ that can be obtained by using standard algorithms designed for non-stationary bandit problems. For the bandit meta-learning problem with fixed task length $τ$, we show that the regret of the algorithm is bounded as $\tilde{O}(NM\sqrt{M τ}+N^{2/3}Mτ)$. Under additional assumptions on the identifiability of the optimal arms in each task, we show a bandit meta-learning algorithm with an improved $\tilde{O}(N\sqrt{M τ}+N^{1/2}\sqrt{M K τ})$ regret.
Intelligent Vision-based Autonomous Ship Landing of VTOL UAVs
Venue: arXiv cs.RO 2022
Year: 2022

The paper discusses an intelligent vision-based control solution for autonomous tracking and landing of Vertical Take-Off and Landing (VTOL) capable Unmanned Aerial Vehicles (UAVs) on ships without utilizing GPS signal. The central idea involves automating the Navy helicopter ship landing procedure where the pilot utilizes the ship as the visual reference for long-range tracking; however, refers to a standardized visual cue installed on most Navy ships called the "horizon bar" for the final approach and landing phases. This idea is implemented using a uniquely designed nonlinear controller integrated with machine vision. The vision system utilizes machine learning-based object detection for long-range ship tracking and classical computer vision for the estimation of aircraft relative position and orientation utilizing the horizon bar during the final approach and landing phases. The nonlinear controller operates based on the information estimated by the vision system and has demonstrated robust tracking performance even in the presence of uncertainties. The developed autonomous ship landing system was implemented on a quad-rotor UAV equipped with an onboard camera, and approach and landing were successfully demonstrated on a moving deck, which imitates realistic ship deck motions. Extensive simulations and flight tests were conducted to demonstrate vertical landing safety, tracking capability, and landing accuracy.
Weakly Supervised Instance Segmentation using Motion Information via Optical Flow
Venue: arXiv cs.CV 2022
Year: 2022

Weakly supervised instance segmentation has gained popularity because it reduces high annotation cost of pixel-level masks required for model training. Recent approaches for weakly supervised instance segmentation detect and segment objects using appearance information obtained from a static image. However, it poses the challenge of identifying objects with a non-discriminatory appearance. In this study, we address this problem by using motion information from image sequences. We propose a two-stream encoder that leverages appearance and motion features extracted from images and optical flows. Additionally, we propose a novel pairwise loss that considers both appearance and motion information to supervise segmentation. We conducted extensive evaluations on the YouTube-VIS 2019 benchmark dataset. Our results demonstrate that the proposed method improves the Average Precision of the state-of-the-art method by 3.1.
Characterization of a Meso-Scale Wearable Robot for Bathing Assistance
Venue: arXiv cs.RO 2022
Year: 2022

Robotic bathing assistance has long been considered an important and practical task in healthcare. Yet, achieving flexible and efficient cleaning tasks on the human body is challenging, since washing the body involves direct human-robot physical contact and simple, safe, and effective devices are needed for bathing and hygiene. In this paper, we present a meso-scale wearable robot that can locomote along the human body to provide bathing and skin care assistance. We evaluated the cleaning performance of the robot system under different scenarios. The experiments on the pipe show that the robot can achieve cleaning percentage over 92% with two types of stretchable fabrics. The robot removed most of the debris with average values of 94% on a human arm and 93% on a manikin torso. The results demonstrate that the robot exhibits high performance in cleaning tasks.
Model Checking on Interpretations of Classes of Bounded Local Cliquewidth
Venue: arXiv cs.DS 2022
Year: 2022

We present a fixed-parameter tractable algorithm for first-order model checking on interpretations of graph classes with bounded local cliquewidth. Notably, this includes interpretations of planar graphs, and more generally, of classes of bounded genus. To obtain this result we develop a new tool which works in a very general setting of dependent classes and which we believe can be an important ingredient in achieving similar results in the future.
A New BAT and PageRank algorithm for Propagation Probability in Social Networks
Venue: arXiv cs.SI 2022
Year: 2022

Social networks have increasingly become important and popular in modern times. Moreover, the influence of social networks plays a vital role in various organizations including government organizations, academic research or corporate organizations. Therefore, how to strategize the optimal propagation strategy in social networks has also become more important. By increasing the precision of evaluating the propagation probability of social network, it can indirectly influence the investment of cost, manpower and time for information propagation to achieve the best return. This study proposes a new algorithm, which includes a scale-free network, Barabasi-Albert model, Binary-Addition-Tree (BAT) algorithm, PageRank algorithm, personalized PageRank algorithm and a new BAT algorithm, to calculate the propagation probability in social networks. The results obtained after implementing the simulation experiment of social network models show the studied model and the proposed algorithm provide an effective method to increase the efficiency of information propagation in social networks. In this way, the maximum propagation efficiency is achieved with the minimum investment.
Initialization of Latent Space Coordinates via Random Linear Projections for Learning Robotic Sensory-Motor Sequences
Venue: arXiv cs.LG 2022
Year: 2022

Robot kinematics data, despite being a high dimensional process, is highly correlated, especially when considering motions grouped in certain primitives. These almost linear correlations within primitives allow us to interpret the motions as points drawn close to a union of low-dimensional linear subspaces in the space of all motions. Motivated by results of embedding theory, in particular, generalizations of Whitney embedding theorem, we show that random linear projection of motor sequences into low dimensional space loses very little information about structure of kinematics data. Projected points are very good initial guess for values of latent variables in generative model for robot sensory-motor behaviour primitives. We conducted series of experiments where we trained a recurrent neural network to generate sensory-motor sequences for robotic manipulator with 9 degrees of freedom. Experimental results demonstrate substantial improvement in generalisation abilities for unobserved samples in the case of initialization of latent variables with random linear projection of motor data over initialization with zero or random values. Moreover, latent space is well-structured wherein samples belonging to different primitives are well separated from the onset of training process.
Bi-directional Joint Neural Networks for Intent Classification and Slot Filling
Venue: arXiv cs.CL 2022
Year: 2022

Intent classification and slot filling are two critical tasks for natural language understanding. Traditionally the two tasks proceeded independently. However, more recently joint models for intent classification and slot filling have achieved state-of-the-art performance, and have proved that there exists a strong relationship between the two tasks. In this paper, we propose a bi-directional joint model for intent classification and slot filling, which includes a multi-stage hierarchical process via BERT and bi-directional joint natural language understanding mechanisms, including intent2slot and slot2intent, to obtain mutual performance enhancement between intent classification and slot filling. The evaluations show that our model achieves state-of-the-art results on intent classification accuracy, slot filling F1, and significantly improves sentence-level semantic frame accuracy when applied to publicly available benchmark datasets, ATIS (88.6%) and SNIPS (92.8%).
Visual Speech Recognition for Multiple Languages in the Wild
Venue: arXiv cs.CV 2022
Year: 2022

Visual speech recognition (VSR) aims to recognize the content of speech based on lip movements, without relying on the audio stream. Advances in deep learning and the availability of large audio-visual datasets have led to the development of much more accurate and robust VSR models than ever before. However, these advances are usually due to the larger training sets rather than the model design. Here we demonstrate that designing better models is equally as important as using larger training sets. We propose the addition of prediction-based auxiliary tasks to a VSR model, and highlight the importance of hyperparameter optimization and appropriate data augmentations. We show that such a model works for different languages and outperforms all previous methods trained on publicly available datasets by a large margin. It even outperforms models that were trained on non-publicly available datasets containing up to to 21 times more data. We show, furthermore, that using additional training data, even in other languages or with automatically generated transcriptions, results in further improvement.
Use of Set Shaping theory in the development of locally testable codes
Venue: arXiv cs.IT 2022
Year: 2022

In developing locally testable codes, information is added to the coded message by creating redundancy in the codewords. In this article, we propose an alternative method in which redundancy is introduced on the message that must be transmitted before its encoding and not in the codewords. This approach exploits the Set Shaping Theory whose goal is the study the bijection functions f(X)=Y that transform a set of strings into a set of equal size made up of strings of greater length. In this way, this type of function transforms the independent variable x into the dependent variable y whose emission probability is conditioned by the previously emitted variables. Thus, if the decoder decodes a symbol associated with a conditional probability equal to zero, we detect an error in the message. If the function f used is the one that minimizes the average information content, we develop a code that can be tested efficiently. In fact, it is observed, in terms of compression, that the greater length of the strings is compensated by the fact of having chosen the strings with less entropy.